Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation

Solar Irradiance Forecasting with Natural Language Processing of Cloud Observations and Interpretation of Results with Modified Shapley Additive Explanations

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth’s surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model’s output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry

Advanced Algorithms in Automatic Generation Control of Hydroelectric Power Plants

The problem of load distribution between hydraulic units at hydropower plants is a difficult task due to the nonlinearity of hydro turbine characteristics and individual peculiarities of the generation units, in which operating conditions are often different. It is necessary to apply the most up-to-date optimization methods that take into account the nonlinearity of the turbine characteristics. The methods must also consider strict constraints on the operation conditions of the power equipment when searching for the extremum of the objective function specified in the form of equalities and inequalities. When solving the aforementioned optimization problem, the constraints on computing capacities of the digital automatic generation control systems that must operate in real-time mode were taken into account. To solve the optimization task, the interior point method was analyzed and the method of Lagrange multipliers was modified so that it could minimize turbine discharge and active energy losses in the windings of the power generators and unit power transformers. The article presents the simulation results of the developed optimization algorithms and the results of the field tests of the automatic generation control system executing the proposed algorithms. All of the tests showed a fairly high efficiency of the proposed optimization methods in real operation conditions